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Zoom metalens doublet exhibits unprecedented advantages over the traditional zoom lens in many aspects, like ultra-compactness, CMOS compatibility, and miniaturization. However, most of them can only work in a narrow band due to chromatic aberration. Here, we numerically demonstrate a broadband achromatic zoom metalens doublet inspired by the Moir\'e lens principle. This design is achieved by globally optimizing the multi-wavelength phase profiles of the metalens doublet in the visible. The doublet can achieve achromatic continuous 1-10 zoom range at wavelengths of 440 nm, 540 nm, and 640 nm, featuring high focusing efficiency up to 86.5% and polarization insensitivity., Comment: 4 pages, 5 figures

Multiplexed planar printings, made of single or few layer micro and nano optical platforms, are essential for high capacity display, information storage and encryption. Although having been developed rapidly, the demonstrated channels are still limited and also lack instantaneity. Here, holograms and printings, always regarded as two independent information coding domains with totally different principles, are combined together through our proposed angle multiplexing framework, leading to multiplexed printings with hundreds of channels. Based on such approach, we experimentally encode respectively 25 gray scale printings into 25 angles and even 8 gray scale videos into 8 angles with a phase-only spatial light modulator. As a bridge between printings and holograms, our method allows to generate printings combining various holographic methods. Beneficial from this, we demonstrate a gradient metasurface based 324 channel printing which multiplexes angles, polarizations and wavelengths simultaneously. Our work paves the way to flexibly angle-dependent printing display and massively multiplexed encryption systems., Comment: 16 pages, 5 figures

We investigate the behavior in time of the energy current between a quantum spin chain and its surrounding non-Markovian, finite temperature baths, together with its relationship to the coherence dynamics of the system. To be specific, both the system and the baths are assumed to be initially in thermal equilibrium at temperature $T_s$ and $T_b$, respectively. This model plays a fundamental role for the study of quantum system evolution towards thermal equilibrium in an open system. The non-Markovian quantum state diffusion (NMQSD) equation approach is used to calculate the dynamics of the spin chain. The effects of bath non-Markovinity, temperature difference and system-bath interaction strength on the energy current and the coherence in warm and cold baths are analyzed, respectively. For both cases, our calculation results show that strong non-Markovianity, weak system-bath interaction and low temperature difference will be helpful to maintain the coherence of the system and correspond to a small energy current. Interestingly, the warm baths destroy the coherence while the cold baths help to generate coherence. Furthermore, the effects of the Dzyaloshinskii-Moriya ($DM$) interaction and the external magnetic field on the energy current and coherence are analyzed. Both energy current and coherence will change due to the improvement of the system energy induced by the $DM$ interaction and magnetic field. Significantly, the lowest coherence corresponds to the critical magnetic field which causes the first order phase transition., Comment: 8 pages, 5 figures

Accurate sensor calibration is a prerequisite for multi-sensor perception and localization systems for autonomous vehicles. The intrinsic parameter calibration of the sensor is to obtain the mapping relationship inside the sensor, and the extrinsic parameter calibration is to transform two or more sensors into a unified spatial coordinate system. Most sensors need to be calibrated after installation to ensure the accuracy of sensor measurements. To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods. OpenCalib covers manual calibration tools, automatic calibration tools, factory calibration tools, and online calibration tools for different application scenarios. At the same time, to evaluate the calibration accuracy and subsequently improve the accuracy of the calibration algorithm, we released a corresponding benchmark dataset. This paper introduces various features and calibration methods of this toolbox. To our knowledge, this is the first open-sourced calibration codebase containing the full set of autonomous-driving-related calibration approaches in this area. We wish that the toolbox could be helpful to autonomous driving researchers. We have open-sourced our code on GitHub to benefit the community. Code is available at https://github.com/PJLab-ADG/SensorsCalibration., Comment: 16 pages, 31 figures

Due to the sequential nature of the successive-cancellation (SC) algorithm, the decoding of polar codes suffers from significant decoding latencies. Fast SC decoding is able to speed up the SC decoding process, by implementing parallel decoders at the intermediate levels of the SC decoding tree for some special nodes with specific information and frozen bit patterns. To further improve the parallelism of SC decoding, this paper present a new class of special nodes composed of a sequence of rate one or single-parity-check (SR1/SPC) nodes, which can be easily found especially in high-rate polar code and is able to envelop a wide variety of existing special node types. Then, we analyse the parity constraints caused by the frozen bits in each descendant node, such that the decoding performance of the SR1/SPC node can be preserved once the parity constraints are satisfied. Finally, a generalized fast decoding algorithm is proposed to decode SR1/SPC nodes efficiently, where the corresponding parity constraints are taken into consideration. Simulation results show that the proposed decoding algorithm of the SR1/SPC node can achieve near-ML performance, and the overall decoding latency can be reduced by 43.8% as compared to the state-of-the-art fast SC decoder., Comment: 30 pages, 6 figures, submitted for possible journal publication

Retrosynthesis analysis is a critical task in organic chemistry central to many important industries. Previously, various machine learning approaches have achieved promising results on this task by representing output molecules as strings and autoregressively decoded token-by-token with generative models. Text generation or machine translation models in natural language processing were frequently utilized approaches. The token-by-token decoding approach is not intuitive from a chemistry perspective because some substructures are relatively stable and remain unchanged during reactions. In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach. Our approach achieved improvement over previously reported models, and we find that the performance can be further boosted if the accuracy of substructure extraction is improved. The substructures extracted by our approach can provide users with better insights for decision-making compared to existing methods. We hope this work will generate interest in this fast growing and highly interdisciplinary area on retrosynthesis prediction and other related topics., Comment: Work in progress

The compiling of quantum gates is crucial for the successful quantum algorithm implementations. The environmental noise as well as the bandwidth of control pulses pose a challenge to precise and fast qubit control, especially in a weakly anharmonic system. In this work, we propose an algorithm to approximately compile single-qubit gates with arbitrary accuracy. Evaluation results show that the overall rotation distance generated by our algorithm is significantly shorter than the commonly used $U3$ gate, then the gate time can be effectively shortened. The requisite number of pulses and the runtime of scheme design scale up as $\mathcal{O}[\mathrm{Log}(1/\epsilon)]$ with very small prefactors, indicating low overhead costs. Moreover, we explore the trade-off between effectiveness and cost, and find a balance point. In short, our work opens a new avenue for efficient quantum algorithm implementations with contemporary quantum technology., Comment: 5 pages, 2 figures

The implementation of automatic image registration is still difficult in various applications. In this paper, an automatic image registration approach through line-support region segmentation and geometrical outlier removal (ALRS-GOR) is proposed. This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations. To begin with, line-support regions, namely a straight region whose points share roughly the same image gradient angle, are extracted to address the issues of inconsistent content existing in images. To alleviate the incompleteness of line segments, an iterative strategy with multi-resolution is employed to preserve global structures that are masked at full resolution by image details or noise. Then, Geometrical Outlier Removal (GOR) is developed to provide reliable feature point matching, which is based on affineinvariant geometrical classifications for corresponding matches initialized by SIFT. The candidate outliers are selected by comparing the disparity of accumulated classifications among all matches, instead of conventional methods which only rely on local geometrical relations. Various image sets have been considered in this paper for the evaluation of the proposed approach, including aerial images with simulated affine deformations, remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal), and map images with inconsistent annotations. Experimental results demonstrate the superior performance of the proposed method over the existing approaches for the whole data set.

Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper,a robust feature point matching algorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine invariant descriptor called vertex trichotomy descriptor is proposed on the basis of that geometrical relations between any of vertices and lines are preserved after affine transformations, which is constructed by mapping each vertex into trichotomy sets. The outlier removals in Vertex Trichotomy Matching (VTM) are implemented by iteratively comparing the disparity of corresponding vertex trichotomy descriptors. Some inliers mistakenly validated by a large amount of outliers are removed in VTM iterations, and several residual outliers close to correct locations cannot be excluded with the same graph structures. Therefore, a recovery and filtering strategy is designed to recover some inliers based on identical vertex trichotomy descriptors and restricted transformation errors. Assisted with the additional recovered inliers, residual outliers can also be filtered out during the process of reaching identical graph for the expanded vertex sets. Experimental results demonstrate the superior performance on precision and stability of this algorithm under various conditions, such as remote sensing images with large transformations, duplicated patterns, or inconsistent spectral content.